Data that forms the basis of many of our daily activities like speech, text, videos has sequential/temporal dependencies. Traditional deep learning models, being inadequate to model this connectivity needed to be made recurrent before they brought technologies such as voice assistants (Alexa, Siri) or video based speech translation (Google Translate) to a practically usable form by reducing the Word Error Rate (WER) significantly. RNNs solve this problem by adding internal memory. The capacities of traditional neural networks are bolstered with this addition and the results outperform the conventional ML techniques wherever the temporal dynamics are more important. In this full-day immersive workshop, participants will develop an intuition for sequence models through hands-on learning along with the mathematical premise of RNNs.

Public Feedback

This looks very promising, how about changing the name to remove RNNs and give something more generic like sequential neural network models \ deep learning models or something which basically means you are covering all types of sequential NN models

Maybe you can also add in bi-directional GRUs\LSTMs since they have proven to work well these days specially w.r.t NLP problems also

Also maybe sequence-to-sequence (encoder-decoder models) like the ones used in neural machine translation

We initially considered doing a workshop on all sequence modeling techniques, however decided against it. From last year's workshops, we saw that attendees are at a broad range of expertise. And for a theory+hands-on session, it is really hard to cover multiple concepts in reasonable depth within the span of a day.

Hence, we will be doing theory and hands-on for RNNs and cover LSTM, bi-directional LSTM and GRU at conceptual level (mentioned in the outline).But we didn't put it explicitly in the title because we thought that the title could be seen as misleading if we include those. We don't want to over-commit and under-deliver. It would be rather good to do the other way around. Hope that explains our thinking.

Also, since there are quite a few proposals which are based on NLP, we thought we should rather focus on the techniques applied in other domains e.g. genomics. But encoder-decoder can certainly be added if that makes more sense.

schedule 4 months ago

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45 Mins

Tutorial

Intermediate

The field of Artificial Intelligence powered by Machine Learning and Deep Learning has gone through some phenomenal changes over the last decade. Starting off as just a pure academic and research-oriented domain, we have seen widespread industry adoption across diverse domains including retail, technology, healthcare, science and many more. More than often, the standard toolbox of machine learning, statistical or deep learning models remain the same. New models do come into existence like Capsule Networks, but industry adoption of the same usually takes several years. Hence, in the industry, the main focus of data science or machine learning is more ‘applied’ rather than theoretical and effective application of these models on the right data to solve complex real-world problems is of paramount importance.

A machine learning or deep learning model by itself consists of an algorithm which tries to learn latent patterns and relationships from data without hard-coding fixed rules.Hence, explaining how a model works to the business always poses its own set of challenges.There are some domains in the industry especially in the world of finance like insurance or banking where data scientists often end up having to use more traditional machine learning models (linear or tree-based).The reason being that model interpretability is very important for the business to explain each and every decision being taken by the model.However, this often leads to a sacrifice in performance. This is where complex models like ensembles and neural networks typically give us better and more accurate performance (since true relationships are rarely linear in nature).We, however, end up being unable to have proper interpretations for model decisions.

To address and talk about these gaps, I will take a conceptual yet hands-on approach where we will explore some of these challenges in-depth about explainable artificial intelligence (XAI) and human interpretable machine learning and even showcase with some examples using state-of-the-art model interpretation frameworks in Python!

Anant Jain - Adversarial Attacks on Neural Networks

schedule 3 months ago

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45 Mins

Talk

Intermediate

Since 2014, adversarial examples in Deep Neural Networks have come a long way. This talk aims to be a comprehensive introduction to adversarial attacks including various threat models (black box/white box), approaches to create adversarial examples and will include demos. The talk will dive deep into the intuition behind why adversarial examples exhibit the properties they do — in particular, transferability across models and training data, as well as high confidence of incorrect labels. Finally, we will go over various approaches to mitigate these attacks (Adversarial Training, Defensive Distillation, Gradient Masking, etc.) and discuss what seems to have worked best over the past year.

Dat Tran - Image ATM - Image Classification for Everyone

schedule 3 months ago

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45 Mins

Talk

Intermediate

At idealo.de we store and display millions of images. Our gallery contains pictures of all sorts. You’ll find there vacuum cleaners, bike helmets as well as hotel rooms. Working with huge volume of images brings some challenges: How to organize the galleries? What exactly is in there? Do we actually need all of it?

To tackle these problems you first need to label all the pictures. In 2018 our Data Science team completed four projects in the area of image classification. In 2019 there were many more to come. Therefore, we decided to automate this process by creating a software we called Image ATM (Automated Tagging Machine). With the help of transfer learning, Image ATM enables the user to train a Deep Learning model without knowledge or experience in the area of Machine Learning. All you need is data and spare couple of minutes!

In this talk we will discuss the state-of-art technologies available for image classification and present Image ATM in the context of these technologies. We will then give a crash course of our product where we will guide you through different ways of using it - in shell, on Jupyter Notebook and on the Cloud. We will also talk about our roadmap for Image ATM.

schedule 4 months ago

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480 Mins

Workshop

Intermediate

Data is the new oil and unstructured data, especially text, images and videos contain a wealth of information. However, due to the inherent complexity in processing and analyzing this data, people often refrain from spending extra time and effort in venturing out from structured datasets to analyze these unstructured sources of data, which can be a potential gold mine. Natural Language Processing (NLP) is all about leveraging tools, techniques and algorithms to process and understand natural language-based data, which is usually unstructured like text, speech and so on. In this workshop, we will be looking at tried and tested strategies, techniques and workflows which can be leveraged by practitioners and data scientists to extract useful insights from text data.

Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity which is expected of any data scientist in today’s fast-paced world! With a hands-on and interactive approach, we will understand essential concepts in NLP along with extensive case- studies and hands-on examples to master state-of-the-art tools, techniques and frameworks for actually applying NLP to solve real- world problems. We leverage Python 3 and the latest and best state-of- the-art frameworks including NLTK, Gensim, SpaCy, Scikit-Learn, TextBlob, Keras and TensorFlow to showcase our examples.

In my journey in this field so far, I have struggled with various problems, faced many challenges, and learned various lessons over time. This workshop will contain a major chunk of the knowledge I’ve gained in the world of text analytics and natural language processing, where building a fancy word cloud from a bunch of text documents is not enough anymore. Perhaps the biggest problem with regard to learning text analytics is not a lack of information but too much information, often called information overload. There are so many resources, documentation, papers, books, and journals containing so much content that they often overwhelm someone new to the field. You might have had questions like ‘What is the right technique to solve a problem?’, ‘How does text summarization really work?’ and ‘Which are the best frameworks to solve multi-class text categorization?’ among many other questions! Based on my prior knowledge and learnings from publishing a couple of books in this domain, this workshop should help readers avoid the pressing issues I’ve faced in my journey so far and learn the strategies to master NLP.

This workshop follows a comprehensive and structured approach. First it tackles the basics of natural language understanding and Python for handling text data in the initial chapters. Once you’re familiar with the basics, we cover text processing, parsing and understanding. Then, we address interesting problems in text analytics in each of the remaining chapters, including text classification, clustering and similarity analysis, text summarization and topic models, semantic analysis and named entity recognition, sentiment analysis and model interpretation. The last chapter is an interesting chapter on the recent advancements made in NLP thanks to deep learning and transfer learning and we cover an example of text classification with universal sentence embeddings.

schedule 1 month ago

45 Mins

Case Study

Intermediate

Over a period of time, a lot of Knowledge bases have evolved. A knowledge base is a structured way of storing information, typically in the following form Subject, Predicate, Object

Such Knowledge bases are an important resource for question answering and other tasks. But they often suffer from their incompleteness to resemble all the data in the world, and thereby lack of ability to reason over their discrete Entities and their unknown relationships. Here we can introduce an expressive neural tensor network that is suitable for reasoning over known relationships between two entities.

With such a model in place, we can ask questions, the model will try to predict the missing data links within the trained model and answer the questions, related to finding similar entities, reasoning over them and predicting various relationship types between two entities, not connected in the Knowledge Graph.

Knowledge Graph infoboxes were added to Google's search engine in May 2012

What is the knowledge graph?

▶Knowledge in graph form!

▶Captures entities, attributes, and relationships

▶More specifically, the “knowledge graph” is a database that collects millions of pieces of data about keywords people frequently search for on the World wide web and the intent behind those keywords, based on the already available content

▶In most cases, KGs is based on Semantic Web standards and have been generated by a mixture of automatic extraction from text or structured data, and manual curation work.

schedule 1 month ago

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45 Mins

Talk

Beginner

The fusion of blockchain and machine learning is an ultimate game changer. Machine learning relies on high volume of data to build models for accurate prediction. A lot of the challenges incurred in getting this data lies in collecting, organizing and auditing the data for accuracy. This is an area that can significantly be improved by using blockchain technology. By using smart contracts, data can be directly and reliably transferred straight from its place of origin. Smart contracts could, however, improve the whole process significantly by using digital signatures.

Blockchain is a good candidate to store sensitive information that should not be modified in any way. Machine learning works on the principle of “Garbage In, Garbage Out,” which means that if the data that was used to build a prediction model was corrupted in any way, the resultant model would not be of much use either. Combining both these technologies creates an industry disruptor which leverages the power of both Blockchain and Machine learning.

schedule 1 month ago

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45 Mins

Tutorial

Beginner

Feature selection is one of the most important processes for pattern recognition, machine learning and data mining problems. A successful feature selection method facilitates improvement of learning model performance and interpretability as well as reduces computational cost of the classifier by dimensionality reduction of the data. Feature selection is computationally expensive and becomes intractable even for few 100 features. This is a relevant problem because text, image and next generation sequence data all are inherently high dimensional. In this talk, I will discuss about few algorithms we have developed in last 5/6 years. Firstly, we will set the context of feature selection ,with some open issues , followed by definition and taxonomy. Which will take about 20 odd minutes. Then in next 20 minutes we will discuss couple of research efforts where we have improved feature selection for textual data and proposed a graph based mechanism to view the feature interaction. After the talk, participants will be appreciate the need of feature selection, the basic principles of feature selection algorithm and finally how they can start developing their own models

schedule 1 month ago

45 Mins

Talk

Intermediate

In traditional machine learning world, data scientist used to spend a considerable amount of time in Data wrangling, model selection and tuning, now with the advances of AutoML it provides methods and processes to make Machine Learning available for non-Machine Learning experts, to improve efficiency of Machine Learning and to accelerate research on Machine Learning.

Automating the end-to-end process of applying machine learning offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform models that were designed by hand.

schedule 1 month ago

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45 Mins

Case Study

Beginner

As the technology progresses, the control tasks are getting increasingly complex. Employing the targeted algorithms for such control tasks and manually tuning them by trial and error (as in case of PID), is a cumbersome and lengthy process. Additionally, methods such as PID are designed for linear systems, however, all the real world control tasks are inherently non-linear in nature. With such complex tasks, using the conventional linear control methods approximates the nonlinear system to a linear model and in effect required performance is difficult to achieve.

The new advances in the field of AI have presented us with techniques which may help replace the traditional control algorithms. Use of AI may allow us to achieve a higher quality of control on the nonlinear process, with minimum human interaction. Thus eliminating the requirement for a skilled person to perform meager tasks of tuning control algorithms with trial and error.

Here we consider a simple case study of a beam balancer, where the controller is used for balancing a beam on a pivot to stabilize the ball at the center of the beam. We aim to implement a Reinforcement Learning based controller as an alternative to PID. We analyze the quality and compare the performance of PID-based controller vs. a RL-based controller to better understand the suitability for real-world control tasks.

Tanay Pant - Machine data: how to handle it better?

schedule 3 months ago

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45 Mins

Talk

Intermediate

The rise of IoT and smart infrastructure has led to the generation of massive amounts of complex data. Traditional solutions struggle to cope with this shift, leading to a decrease in performance and an increase in cost. In this session, I will talk about time-series data, machine data, the challenges of working with this kind of data, ingestion of this data using data from NYC cabs and running real time queries to visualise the data and gather insights. By the end of this session, you will be able to set up a highly scalable data pipeline for complex time series data with real time query performance.

schedule 1 month ago

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45 Mins

Demonstration

Beginner

For many of the researchers and data scientists, Jupyter Notebooks are the de-facto platform when it comes to quick prototyping and exploratory analysis. Right from Paul Romer- the Ex-World bank chief Economist and also the co-winner 2018 Nobel prize in Economics to Netflix, Jupyter Notebooks are used almost everywhere. The browser-based computing environment, coupled with a reproducible document format has made them the choice of tool for millions of data scientists and researchers around the globe. But have we fully exploited the benefits of Jupyter Notebooks and do we know all about the best practises of using it? if not, then this talk is just for you.

Through this talk/demo, I'll like to discuss three main points:

Best Practises for Jupyter Notebooks since a lot of Jupyter functionalities sometimes lies under the hood and is not adequately explored. We will try and explore Jupyter Notebooks’ features which can enhance our productivity while working with them.

In this part, we get acquainted with Jupyter Lab, the next-generation UI developed by the Project Jupyter team, and its emerging ecosystem of extensions. JupyterLab differs from Jupyter Notebook in the fact that it provides a set of core building blocks for interactive computing (e.g. notebook, terminal, file browser, console) and well-designed interfaces for them that allow users to combine them in novel ways. The new interface enables users to do new things in their interactive computing environment, like tiled layouts for their activities, dragging cells between notebooks, and executing markdown code blocks in a console and many more cool things.

Every tool/features come with their set of pros and cons and so does Jupyter Notebooks/Lab and it is equally important to discuss the pain areas along with the good ones.

JAYA SUSAN MATHEW - Breaking the language barrier: how do we quickly add multilanguage support in our AI application?

schedule 4 months ago

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20 Mins

Talk

Intermediate

With the need to cater to a global audience, there is a growing demand for applications to support speech identification/translation/transliteration from one language to another. This session aims at introducing the audience to the topic and how to quickly use some of the readily available APIs to identify, translate or even transliterate speech/text within their application.

schedule 2 months ago

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45 Mins

Demonstration

Intermediate

Knowledge representation has been a research for many years in AI world and its continuing further too. Once knowledge is represented, reasoning from that extracted knowledge is done by various inferencing techniques. Initial knowledge bases were built using rules from domain experts and different inferencing techniques like Fuzzy inference, Bayesian inference were applied to extract reasoning from those knowledge bases. Semantic networks is another form of knowledge representation which can represent structured data like WordNet, DBpedia which solves problems in a specific domain by storing entities and relations among entities using onotologies.

Knowledge graph is another representation technique deeply researched in academia as well as used by businesses in production to augment search relevancy in information retrieval(Google knowledgegraph), improve recommender systems, semantic search applications and also Question answering problems.In this talk i will illustrate the benefits of semantic knowledge graph, how it differs from Semantic ontologies, different technologies involved in building knowledge graph, how i built one to analyse unstructured (twitter data) to discover hidden relationships from the twitter corpus. I will also show how Knowledge graph is data scientist's tool kit to discover hidden relationships and insights from unstructured data quickly.

In this talk/demonstration i will show the work done to determine entity reputation and entity co-occurence using Knowledge graph.Scoring an entity for reputation is useful in many Natural language processing tasks and applications such as Recommender systems.